To compare with the relevant state-of-the-art, we follow the common setting in ([1]}, [2]}) to train deep CNNs,
where the label of each image is independently changed to a uniform random class with probability \(p\) , where \(p\)  is noise fraction.
The labels of validation data remain clean for evaluation.
We compare our approach with recent state-of-the-art approaches in this setting [1]}, [2]}, [5]}, [6]}.
MentorNet [1]} and Meta-Weight-Net [6]} train an auxillary neural network to assign weights to samples in the mini-batch.
L2RW [2]} uses a held-out set to perform an online approximation of weights for samples in the mini-batch.
Data parameters [5]} introduced learnable temperature parameters per data-point, which scale the gradient contribution of each data point.
Unfortunately, all of these works report results in two distinct settings: setting A [2]} and setting B [6]}.
While both settings use WRN-28-10, they differ in learning rate schedules (see details in supplementary).
To make a fair comparion, we report results in both settings.
Similar to setup in [2]}, [6]}, we keep 1000 clean images as meta-set. As seen in Figure REF , our method
outperforms other state-of-the-art methods in both settings under different levels of noise.
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